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Netlab Reference Manual mlp
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<H1> mlp
</H1>
<h2>
Purpose
</h2>
Create a 2-layer feedforward network.

<p><h2>
Synopsis
</h2>
<PRE>
net = mlp(nin, nhidden, nout, func)
net = mlp(nin, nhidden, nout, func, prior)
net = mlp(nin, nhidden, nout, func, prior, beta)
</PRE>


<p><h2>
Description
</h2>
<CODE>net = mlp(nin, nhidden, nout, func)</CODE> takes the number of inputs, 
hidden units and output units for a 2-layer feed-forward network,
together with a string <CODE>func</CODE> which specifies the output unit
activation function, and returns a data structure <CODE>net</CODE>. The
weights are drawn from a zero mean, unit variance isotropic Gaussian,
with varianced scaled by the fan-in of the hidden or output units as
appropriate. This makes use of the Matlab function
<CODE>randn</CODE> and so the seed for the random weight initialization can be 
set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the seed value. 
The hidden units use the <CODE>tanh</CODE> activation function.

<p>The fields in <CODE>net</CODE> are
<PRE>

  type = 'mlp'
  nin = number of inputs
  nhidden = number of hidden units
  nout = number of outputs
  nwts = total number of weights and biases
  actfn = string describing the output unit activation function:
      'linear'
      'logistic
      'softmax'
  w1 = first-layer weight matrix
  b1 = first-layer bias vector
  w2 = second-layer weight matrix
  b2 = second-layer bias vector
</PRE>

Here <CODE>w1</CODE> has dimensions <CODE>nin</CODE> times <CODE>nhidden</CODE>, <CODE>b1</CODE> has
dimensions <CODE>1</CODE> times <CODE>nhidden</CODE>, <CODE>w2</CODE> has
dimensions <CODE>nhidden</CODE> times <CODE>nout</CODE>, and <CODE>b2</CODE> has
dimensions <CODE>1</CODE> times <CODE>nout</CODE>.

<p><CODE>net = mlp(nin, nhidden, nout, func, prior)</CODE>, in which <CODE>prior</CODE> is
a scalar, allows the field <CODE>net.alpha</CODE> in the data structure
<CODE>net</CODE> to be set, corresponding to a zero-mean isotropic Gaussian
prior with inverse variance with value <CODE>prior</CODE>. Alternatively,
<CODE>prior</CODE> can consist of a data structure with fields <CODE>alpha</CODE>
and <CODE>index</CODE>, allowing individual Gaussian priors to be set over
groups of weights in the network. Here <CODE>alpha</CODE> is a column vector
in which each element corresponds to a separate group of weights,
which need not be mutually exclusive.  The membership of the groups is
defined by the matrix <CODE>indx</CODE> in which the columns correspond to
the elements of <CODE>alpha</CODE>. Each column has one element for each
weight in the matrix, in the order defined by the function
<CODE>mlppak</CODE>, and each element is 1 or 0 according to whether the
weight is a member of the corresponding group or not. A utility
function <CODE>mlpprior</CODE> is provided to help in setting up the
<CODE>prior</CODE> data structure.

<p><CODE>net = mlp(nin, nhidden, nout, func, prior, beta)</CODE> also sets the 
additional field <CODE>net.beta</CODE> in the data structure <CODE>net</CODE>, where
beta corresponds to the inverse noise variance.

<p><h2>
See Also
</h2>
<CODE><a href="mlpprior.htm">mlpprior</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</a></CODE>, <CODE><a href="mlpfwd.htm">mlpfwd</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpbkp.htm">mlpbkp</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
<hr>
<p>Copyright (c) Ian T Nabney (1996-9)


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